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林业科学 ›› 2024, Vol. 60 ›› Issue (2): 1-11.doi: 10.11707/j.1001-7488.LYKX20220526

• 研究论文 • 上一篇    下一篇

不同产区杉木生物量与碳储量模型

吕梓晴1, 段爱国1,2   

  1. 1. 林木资源高效生产全国重点实验室 国家林业和草原局林木培育重点实验室 中国林业科学研究院林业研究所 北京 100091;
    2. 南京林业大学 南方现代林业协同创新中心 南京 210037
  • 收稿日期:2022-08-01 修回日期:2023-04-06 发布日期:2024-03-13
  • 通讯作者: 段爱国
  • 基金资助:
    “十四五”国家重点研发计划项目“杉木用材林定向培育技术研究(2021YFD2201301)”

Biomass and Carbon Storage Model of Cunninghamia lanceolata in Different Production Areas

Lü Ziqing1, Duan Aiguo1,2   

  1. 1. State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration Research Institute of Forestry,Chinese Academy of Forestry Beijing 100091;
    2. Collaborative Innovation Center of Sustainable Forestry in Southern China Nanjing Forestry University Nanjing 210037
  • Received:2022-08-01 Revised:2023-04-06 Published:2024-03-13

摘要: 目的 建立适用于不同产区的杉木人工林生物量和碳储量模型,以便准确估算杉木人工林生物量和碳储量。方法 基于四川、广西、江西和福建共109株杉木的树干、树皮、枝、叶和根实测生物量数据以及四川、广西和福建共40株杉木的树干、枝、叶和根的含碳量实测数据,建立不同产区、不同林龄和综合产区成熟林的可加性生物量、碳储量模型。采用似乎不相关回归(SUR)对可加性模型系统中的参数进行联合估计,并用调整后确定系数R2a和总相对误差TRE检验模型拟合精度。结果 1) 4个产区和不同林龄杉木生物量模型的R2a为0.635 0~0.995 8,TRE为?17.88%~21.39%,树干、树皮和全株生物量模型的R2a均在0.91以上,适用于建模地的杉木人工林生物量预测。广西分侧根拟合的生物量模型除一级侧根外,R2a均在0.80以上,TRE为?5.42%~7.21%,可用于预测广西杉木人工林侧根生物量。枝、叶、根的生物量模型拟合精度较干、树皮低。2) 四川、广西和福建3个产区碳储量模型R2a为0.805 0~0.994 0,TRE为?19.34%~19.84%,树干、根和全株模型R2a在0.93以上,适用于各地区杉木人工林碳储量预测。枝、叶的碳储量模型拟合精度较干和根低。3) 不同产区的生物量、碳储量模型通用性存在地域差异,位于中亚热带西区的四川生物量模型通用性最差,位于南亚热带的广西带皮干和全株生物量模型通用性较好,位于中亚热带东区的福建和江西生物量模型可进行相互预测;南亚热带广西的碳储量模型通用性最好,而四川和福建的碳储量模型仅适用于本地碳储量预测。4) 综合生物量模型R2a为0.733 5~0.966 9,根据交互检验结果,综合模型可准确估算不同产区成熟林和福建幼龄林、中龄林的带皮干和全株生物量,TRE为?10.47%~19.88%;还可对江西和福建成熟林及福建中龄林除枝以外的各器官和全株生物量进行准确预测;综合碳储量模型R2a为0.802 9~0.982 6,除对广西杉木人工林枝碳储量的预测误差相对较大以外,其他检验样本TRE为?9.57%~15.70%,说明模型的通用性好,可准确预测不同产区的杉木人工林各器官和全株碳储量。结论 本研究所建立的模型均适用于建模地生物量和碳储量的预测;模型的通用性受产区差异的影响;综合模型可用于不同地区生物量和碳储量的预测。

关键词: 杉木人工林, 生物量, 碳储量, 不同产区, 可加性模型

Abstract: Objective The purpose of this study is to establish biomass and carbon storage models of Chinese fir plantations suitable for different production areas, so as to provide a basis for accurate estimation of biomass and carbon storage of Chinese fir plantations.Method Based on the measured biomass data of stem, bark, branch, leaf, and root of 109 Chinese fir trees in Sichuan, Guangxi, Jiangxi, and Fujian, and the measured biomass and carbon content of stem, branch, leaf, and root of 40 Chinese fir trees in Sichuan, Guangxi, and Fujian, the additive biomass and carbon storage models of mature forests in different production areas, different forest ages, and comprehensive production areas were established. The seemingly unrelated regression (SUR) is used to jointly estimate the parameters in the additive model system, and the fitting accuracy of the model is tested with the adjusted determination coefficient R2a, and the total relative error TRE.Result 1) The R2a of the fir biomass models for the four production areas and different stand ages ranged from 0.635 0 to 0.995 8, with TRE ranging from ?17.88% to 21.39 %, and the R2a of the stem, bark, and whole plant biomass models were above 0.91, which is suitable for biomass prediction of fir plantation forests in the modeled sites. The biomass models fitted to Guangxi sub-lateral roots had R2a above 0.80 and TRE of ?5.42% to 7.21%, except for the first-grade lateral roots, which can be used to predict the biomass of lateral roots in Guangxi fir plantation forests. The biomass model fitting accuracy of branches, leaves, and roots was lower than that of stem and bark. 2) The carbon stock model R2a was from 0.805 0 to 0.994 0 and TRE was from ?19.34% to 19.84% for the three production areas of Sichuan, Guangxi, and Fujian, and the R2a of the stem, root, and whole plant models was above 0.93, which applied to the prediction of carbon stock in cedar plantation forests in all regions. The carbon stock model fitting accuracy of branches and leaves was lower than that of stem and roots. 3) There are differences in the accuracy of the biomass and carbon stock models across different regions. The biomass model of Sichuan, located in the western part of the central subtropics, has the lowest accuracy. On the other hand, the biomass model of Guangxi, located in the southern subtropics, with the skinned stem and the whole-plant biomass model, has better accuracy. Additionally, the biomass models of Fujian and Jiangxi, located in the eastern part of the central subtropics, have similar accuracy and can be used interchangeably. Regarding the carbon stock models, the one in Guangxi has the highest accuracy, whereas the ones in Sichuan and Fujian are only suitable for predicting the carbon stock in their respective regions. 4) The comprehensive biomass model R2a is 0.733 5?0.966 9. According to the results of the cross-test, the comprehensive model can accurately predict the biomass of stem with bark and the whole plant of mature forests in different production areas, young forests, and middle-aged forests in Fujian, and the TRE is ?10.47%?19.88%. It can also accurately predict the biomass of organs and whole plants except branches of mature forests in Jiangxi, Fujian, and middle-aged forests in Fujian. The comprehensive carbon storage model R2a is 0.802 9?0.982 6. Except for the relatively large prediction error of branch carbon storage of Chinese fir plantations in Guangxi, the TRE of other test samples is ?9.57%?15.70%, indicating that the model has good universality and can accurately predict the carbon storage of various organs and whole plants of Chinese fir plantations in different production areas.Conclusion The models established in this study apply to the prediction of biomass and carbon storage in the modeling site. The universality of the model is affected by the difference in production areas. The integrated model can be used to predict biomass and carbon storage in different regions.

Key words: Chinese fir plantations, biomass, carbon storage, different production areas, additive model

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